Language Learning System Adapted to Personalize Language Learning to Individual Users

ABSTRACT

A learning system and a method adapted to personalize language learning to individual users. An output device generates and presents learning related data to a user associated with a user ID. An input mechanism receives, in response to the learning related data, response data from the user indicating the users response to the learning related data. A processor associates the learning related data to the response data to couple the response from the user to the learning related data. A database including a storage space associated to the user ID is used for storing the learning related and the associated response data and generates an individualized language knowledge database for the user. The processor is adapted to issue a true (t l ) or false (f l ) learning related data indicators indicating whether the response data matches the learning related data presented to the user.

FIELD OF THE INVENTION

The present invention relates to a language learning system and methodadapted to personalize language learning to individual users.

BACKGROUND OF THE INVENTION

Some improvements have been made in language learning for users in pastyears. In one prior art U.S. Pat. No. 6,077,085A an improved method isproposed, a learn, test and review method is used. A spaced reviewsystem is described using layered pool advance spacing were items arearranged in study pools after previous accomplishment, then each pool istargeted for review and advancing to the next review pool is based uponsuccessful review of all items in a given pool making it possible forfew items to slow down the whole study process. This is a discontinuousstudy process; further more effectiveness is limited by grouping itemsinto review pools. In another prior art, U.S. Pat. No. 6,652,283describes a method for maximizing the effectiveness and efficiency oflearning, retaining and retrieving knowledge and skills based onmetacognitive methodology. A Learn Module, a Review Module and a TestModule is provided, where each of these modules are capable of operatingindependently and are preferably arranged to operate interactively suchthat operation of each of the Learn, Review and Test Modules are changedbased on a user's past performance within one or more of the threemodules. Despite the improvements described in U.S. Pat. No. 6,652,283in monitoring and improving the users metacognitive skills, it is notfully tailored to maximize the effectiveness of language learning.Furthermore it is still not a fully continuous learning process, itrequires the user to operate the learn module to learn items, the testmodule to test his memory retention of each item learned and then thereview module to maintain a desired level of memory retention.

The inventor of the present invention has appreciated that there is thusa need for an improved language learning system and a method and has inconsequence devised the present invention.

SUMMARY OF THE INVENTION

It would be advantageous to achieve a language learning system that iscapable of improving the language learning and adapt it to individualusers. In general, the invention preferably seeks to mitigate, alleviateor eliminate one or more of the above mentioned disadvantages singly orin any combination. In particular, it may be seen as an object of thepresent invention to provide a language learning system that solves theabove-mentioned problems, or other problems, of the prior art.

To better address one or more of these concerns, in a first aspect ofthe invention a language learning system is provided adapted topersonalize language learning to individual users, comprising:

-   -   an output means (O_M) for generating and presenting learning        related data to a user associated with a user ID,    -   an input means (I_M) adapted to receive, in response to said        learning related data, response data from the user indicating        the users response to said learning related data,    -   a processor (P) for associating said learning related data to        said response data so as to couple the response from the user to        said learning related data, and    -   a database including a storage space associated to the user ID        for storing said learning related and said associated response        data and thus generating an individualized language knowledge        database for the user,        wherein the processor (P) is further being adapted to:    -   issue a true (t_(l)) or false (f_(l)) learning related data        indicators indicating whether the response data matches the        learning related data presented to the user, the t_(l) or f_(l)        indicators subsequently being associated to said learning        related data and stored at said storage space, monitor said        t_(l) indicators in said storage space and based thereon        repetitively presenting learning related data having associated        t_(l) indicator to the user with user-specific ascending memory        spaced interval which is registered and associated to the        learning related data until a pre-defined time interval limit        has been reached, and based thereon select at least one task to        be presented to the user by said output means,

where within said at least one task plurality of task specific exerciseseach of which includes at least one learning related data where saidpre-defined interval has been reached are presented to the user withtask time ascending intervals in case user's reply to previous taskspecific exercises is correct or satisfactory.

Accordingly, by systematically increasing this user-specific ascendingmemory time interval for the learning related data the system ensuresthat the entries will go “deeper” in the user's memory and in that waythe system will model individual entries for each individual user. Saidpre-defined time interval limit for the learning related data changeswith the user's language skills, i.e. it is adapted to each individualuser and may vary from e.g. few hours up to few days or weeks. Thismeans that for some users that are very good in language learning thesepre-defined time interval limits for the learning related data can beless than for other users, for the same learning related data. Thepre-defined time intervals limits can theoretically then be increasedinto years and even decades, or be decreased into few months or weeksdepending on the user's potential to learn a language. It is thereforepossible to indicate when e.g. certain entries are ready to be used in amore “advanced” level for a certain task. Also, this language learningsystem is capable of linking different usage of e.g. entries with howwell the user memorizes them and in that way it is possible to keeptrack of the user data history which becomes more detailed and gives abetter insight into the user's improvements since now e.g. the entriesthat have been presented to the user are marked with true or falseindicator. The term entry may according to the present invention meanone or more words e.g. “to write”, or a single entry in one language buttwo or more entries in another language. In this case, a single entrymay be associated to two or more words and vice versa. It may also bemany to many mapping, e.g. idioms such as “rain cats and dogs (idiom)”,or, “rignir mjög miki

”, which is Icelandic and means “rains very much” (idiom).

In one embodiment, said process of presenting learning related data bysaid output means includes presenting the learning related data in afirst language and in a second language simultaneously.

In one embodiment, subsequent to present said user with said learningrelated data in said first and second languages the user is presentedwith said learning related data in a first language and at least onesuggestion entry of a learning related data in said second language,said input from the user via said input means being whether saidsuggestion entry corresponds to said learning related in the secondlanguage.

In one embodiment, said at least one task is run parallel andindependent from said process of presenting learning related data bysaid output means, said at least one task being run independent fromeach other such that while presenting said user with said task specificexercises learning related data are presented to the usersimultaneously.

In one embodiment, said processor is operable to start a new taskparallel to said process of presenting learning related data andparallel to the task already being run, said decision of starting saidnew task being based on monitoring said user-specific ascending memoryspaced interval and utilize said memory time as an indicator of how“deep enough” the learning related data is in the user's memory.

In one embodiment, said task time ascending intervals are spacedindividually based on task specific forgetting curves each of the taskspecific forgetting curves being characteristic for each individualtask.

In one embodiment, for each individual user, said processor is furtheroperable to utilize said learning related data presented to the user todetermine a forgetting-curve coefficients for each task, theforgetting-curve coefficients being indicative of how easily eachindividual user remembers new learning related data.

In one embodiment, said exercises within said tasks are formed by amultiple of learning related data including at least one learningrelated data where said pre-defined time interval limit has beenreached, the processor further being adapted to:

-   -   select an exercise in accordance to a set of rules including        selecting at least one of the remaining learning related data in        said task in accordance to said associated user-specific time        intervals so as to optimize and individualize the exercises to        the user,    -   receive, in response to exercise presented to the user, response        data from the user via said input means (I_M) indicating the        users response to said exercise,    -   issue a true-task (t_(t)) or false-task (f_(t)) indicators        indicating whether the response data to said exercise is correct        or not, the t_(t) or f_(t) indicators subsequently being        associated to said task or exercise and stored at said storage        space, and    -   selecting at least one subsequent exercise based on said t_(t)        or f_(t) indicators.

In one embodiment, the exercises within said at least one task areadapted to the individual users with variable time intervals bases onsaid t_(t) or f_(t) indicators.

In one embodiment, said at least one task is selected from:

-   -   a pronunciation task and where said task specific exercises        include playing at least one learning related data where said        pre-determined interval has been reached to the user and where        the user repeats the pronunciation of said learning related        data, said language learning system further comprising a speech        recognition system for processing the pronunciation from the        user so as to determine if the pronunciation is correct or not,        where in case the pronunciation is correct a t_(t) indicator is        associated the word, or    -   listening where the exercises include playing a sound file        displaying a sentence to the user comprising at least one word        where said pre-defined time interval limit has been reached in        the sentence(s) is blank, where the said input means is a key or        touch button command where the user replies to the missing word        in the blank, the input subsequently being processed and        compared with a reference relating data where in case the reply        matches with the missing word a t_(t) indicator is associated to        said task, or    -   reading task where the exercises include presenting the user        with a paragraph including at least one learning related data        where said pre-defined time limit has been reached,    -   a writing task and where said exercises include that the user        writes a sentence including at least one word where said        pre-defined time interval has been reached, said sentence        subsequently being processed and compared with a pre-stored        reference sentences, and where t_(t) or f_(t) indicators are        associated to said task depending on a match or non-match with        said reference sentences, or    -   a conversation task and where the exercises in the conversation        task include initiating a conversation between the user and an        instructor via a network, the instructor being provided with        user specific information including information about        pre-defined time interval limit has been reached, the subject of        the conversation being selected such that it includes at least        one learning related data where said pre-defined time interval        limit has been reached, or    -   a combination of one or more of the above mentioned tasks.

In one embodiment, the processor (P) is further adapted to monitor saidf_(l) indicators in said storage space and based thereon repetitivelypresenting learning related data having associated f_(l) indicators tothe user with user-specific time intervals until t_(l) indicators areissued for the users response, the t_(l) indicators subsequently beingassociated to said learning related data and said associated responsedata in said storage space, said step of presenting learning relateddata having associated t_(l) indicator to the user with user-specificascending memory time interval being repeated until a pre-defined timeinterval limit has been reached.

The present invention thus improves the learning mechanism byestablishing a continuous and non stopping process that automaticallymeasures the memory retention of each item learned. Thus being able toschedule optimal instances for reviews of each item learned and tomaster a never forget state of items learned. Furthermore the prior artmethods do not organize and schedule more complex language learningtasks for each user based on the memory retention of each word learned,whereas the present invention accomplices this using a special processfor this purpose, thus obtaining at individual level (a personalized) amore optimal language learning method than presented before.

Moreover, the system that has one continuous study process measuring andoptimizing each item learned and further scheduling more advancedlanguage learning tasks based on memory retention of each item/wordstudied. Thus, advancing and optimizing the whole language learningprocess beyond what has been done to the present.

Said language learning system may of course be implemented for alllanguage pairs. Also, said task should not be construed as being limitedto the above mentioned embodiments. On the contrary further tasks couldbe defined and for different language pairs, the sequence for exercisingthese tasks could be different.

The system is thus capable of selecting various types of tasks, e.g.choose a sound file that may include a sentence, paragraph, short story,etc., where while listening, the user can also read the text that isbeing played. By letting the user e.g. write the entries in the blanksthat are in “focus”, i.e. entries where said pre-defined time intervallimit has been reached, and possibly some other entries as well whilelistening the system can judge if the user was able to grasp the correctlistening or not. Therefore, the language learning will further beimproved and adapted for each individual user.

Said sound files in the listening tasks is preferably chosen by usingsimilar logic as selecting the “best” example sentence as discussedlater, i.e. based on what the system thinks that the user is prepared tolisten to. This is based on the user data history stored at said storagespace that is associated to the user where said associated timeintervals may be used for selecting the most appropriate sentence to beplayed for the user. This listening material may be longer than just asentence, e.g. news, short story, long dialogues etc., where theselection is based on what the system considers as being most suitablefor the user. This could be today's news story on the Japan earthquakewhere the system deems that the user is ready to read or listen to thestory. Another story, e.g. today's story on Libya, may be considered asnot being ready for the user to be read or listen to since the userstill doesn't know X number of entries found in the news story and Ynumber of entries, which are known to the user, still need to be broughtto a Z day spaced interval so that the user is able to make sense out ofthem in reading or listening.

In a second aspect of the invention, a method of personalizing languagelearning to individual users, comprising:

-   -   generating and presenting learning related data to a user        associated with a user ID,    -   receiving, in response to said learning related data, response        data from the user indicating the users response to said        learning related data,    -   associating said learning related data to said response data so        as to couple the response from the user to said learning related        data, and    -   storing said learning related and said associated response data        in a database including a storage space associated to the user        ID so as to generate an individualized language knowledge        database for the user,        wherein the method further comprises:    -   issuing a true (t_(l)) or false (f_(l)) learning related data        indicators indicating whether the response data matches the        learning related data presented to the user, the t_(l) or f_(l)        indicators subsequently being associated to said learning        related data and stored at said storage space,    -   monitoring said t_(l) indicators in said storage space and based        thereon repetitively presenting learning related data having        associated t_(l) indicator to the user with user-specific        ascending memory spaced interval which is registered and        associated to the learning related data until a pre-defined time        interval limit has been reached, and based thereon select at        least one task to be presented to the user by said output means,

where within said at least one task plurality of task specific exerciseseach of which includes at least one learning related data where saidpre-defined interval has been reached are presented to the user withtask time ascending intervals in case user's reply to previous taskspecific exercises is correct or satisfactory.

In a third aspect of the invention a computer program is providedcomprising instructions for carrying out all the above mentioned stepswhen said computer program is executed on a computer system.

In a forth aspect of the invention a computer program product isprovided computer program product tangibly embodied on acomputer-readable medium and including executable code that, whenexecuted, is configured to cause one or more processors of a computingdevice to:

-   -   generate and present learning related data to a user associated        with a user ID,    -   receive, in response to said learning related data, response        data from the user indicating the users response to said        learning related data,    -   associate said learning related data to said response data so as        to couple the response from the user to said learning related        data,    -   store said learning related and said associated response data in        a database including a storage space associated to the user ID        so as to generate an individualized language knowledge database        for the user,    -   issue a true (t_(l)) or false (f_(l)) learning related data        indicators indicating whether the response data matches the        learning related data presented to the user, the t_(l) or f_(l)        indicators subsequently being associated to said learning        related data and stored at said storage space,    -   monitor said t_(l) indicators in said storage space and based        thereon repetitively presenting learning related data having        associated t_(l) indicator to the user with user-specific        ascending memory spaced interval which is registered and        associated to the learning related data until a pre-defined time        interval limit has been reached, and based thereon select at        least one task to be presented to the user by said output means,    -   where within said at least one task plurality of task specific        exercises each of which includes at least one learning related        data where said pre-defined interval has been reached are        presented to the user with task time ascending intervals in case        user's reply to previous task specific exercises is correct or        satisfactory.

In general the various aspects of the invention may be combined andcoupled in any way possible within the scope of the invention. These andother aspects, features and/or advantages of the invention will beapparent from and elucidated with reference to the embodiments describedhereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention will be described, by way of example only,with reference to the drawings, in which

FIG. 1 depicts graphically a language learning system according to thepresent invention adapted to personalize language learning to individualusers,

FIG. 2 shows a flowchart of a method according to the present inventionfor personalizing language learning to individual users,

FIGS. 3-8 depict graphically scenario where the user is learningIcelandic from English,

FIGS. 9 and 10 depict a scenario where a conversation between a user andan instructor has been initiated,

FIG. 11 depicts one embodiment of the language learning system accordingto the present invention, where part of the system is implemented as acomputer game,

FIGS. 12 and 13 depict an example where a user is learning Japaneseusing an easy flashcard level,

FIG. 14-15 shows a flashcard example where the flashcard level is moreadvanced than in FIGS. 12 and 13 since now the flashcard uses Japanesehiragana or katakana characters,

FIG. 16-17 depicts the scenario where the flashcard level is moreadvanced then in FIGS. 14 and 15 since now the flashcard uses Japanesekanji symbols or a mixture of kanji symbols and hiragana characters,

FIG. 18 depicts so-called forgetting curve,

FIG. 19 depicts the general principle of the present invention, and

FIG. 20 shows several parallel running task process and task milestonesfor an item.

DESCRIPTION OF EMBODIMENTS

The present invention relates to a language learning system and a methodwhere the language learning is personalized for individualized users.The method/system may be divided into three main steps:

1. Memory step: Here the user is presented with learning related datawhich may e.g. be flashcard of a word in focus that is displayed to theuser. This flashcard is presented to the user with time ascending timeinterval if the user answered the previous flashcard correctly. In thefollowing this time ascending interval will be referred to as a memoryspaced interval.2. Task: When the word in focus is “deep enough” in the user's memory, afirst task selected from plurality of tasks is selected for the user.Within each task the user is presented multiple of task specificexercises. This means that each task has its own exercises that arespecific for each task and each of the exercises are spaced according toa task forgetting curve for this specific task. The task forgettingcurves for different tasks are independent from each other and arepreferably based on empirical research or methods as is described later.

This first task runs independent from the memory step procedure meaningthat while the task exercises are presented to the user, the flash cardprocedure is still running simultaneously. Subsequent task(s) are addedto the running task and running memory procedure when its optimal forthe user to start performing this new task. Lets say that the first taskis a pronunciation task and a second task is a listening task, and theword in focus is “pilot”, when the memory depth of the word has reacheda second milestone, which determines when the second task should bestarted, the listening task for the word pilot is started. At this timepoint, there are three procedures running simultaneous, the memoryprocedure, the first task procedure and the second task procedure.Additional task may then further be added as an additional parallelprocedure to the above mentioned running procedures based on third,fourth etc. milestones. This is shown graphically in FIG. 20, where “M”stands for memory retention process (flash cards are presented), “P”stands for pronunciation task, “L” for listening task, “R” for readingtask, “W” for writing task and “C” for conversation task. Points M1-M5stand for different task milestones, M1 is the first task milestone, M2the second etc.

The task milestones are found by empirical methods determining when itis optimal to present a certain task for a given item to a user, basedon the user's memory retention of that given item in order to advancethe user's ability to master a particular language.

FIG. 1 depicts graphically a language learning system 100 according tothe present invention adapted to personalize language learning toindividual users 105 comprising an output means (O_M) 101, an inputmeans (I_M) 102, a processor (P) 103 and a database 104.

The output means (O_M) 101 is adapted to generate and present learningrelated data to a user 105 where the user is associated with a user ID,which may e.g. be username and/or password or data that uniquelyidentify the user. The output means (O_M) 101 may as an example be, butis not limited to, a display that displays the learning related data tothe user, or a speaker that plays the learning related data via speech.

The input means (I_M) 102 is adapted to receive, in response to thelearning related data, response data from the user indicating the user's105 response to the learning related data. The input means (I_M) 102 mayas an example be, but is not limited to, a keyboard or touch-boardmechanism where the user 105 can type his/her response data, a mousewhere via selecting an appropriate icon/flashcard being displayed onsaid display, a speech recognition system where the user 105 can viaspeech indicate his/her response to the learning related data.

The processor (P) 103 is adapted to associate the learning related datato the response data from the user 105 and subsequently store these datain the database 104, which includes a storage space 109 associated tothe user ID. In that way, an individualized language database isgenerated for this specific user 105. The processor (P) 103 is furtheradapted to: i) issue true (t_(l)) or false (f_(l)) indicators indicatingwhether the response data matches the learning related data. Issuingsuch a t_(l) or f_(l) may, as will be discussed in more details later,be done by the user by simply clicking on a right or wrong icon, i.e.the user himself may be the one that tells whether or not the answer iscorrect or not. The t_(l) or f_(l) indicators are associated to thelearning related data and stored in the storage space. ii) Monitor thet_(l) indicators in the storage space 109 and based thereon repetitivelypresenting learning related data having associated t_(l) indicator tothe user with user-specific memory spaced ascending time intervals whichis registered and associated to the learning related data until apre-defined time interval limit has been reached, which may referred toas a first task milestone based on empirical method for the first task.Generally speaking, both the t_(l) and f_(l) indicators are monitoredand stored so as to store as many information from the user as possibleand therefore make a better and more personalized system. Other types ofinformation that may be stored can be, but is not limited to, if a userhovers over an example sentence to get information on a certain entry inthe example sentence, ask for a translation of the example sentence,hover over a kanji symbol to get more information on that kanji symbol,get information (pronunciation, reading, etc.) on a word that is not infocus when in pronunciation mode, etc. Therefore the system gets hintsif the user has possibly trouble with certain things by automaticallyinvestigating the user's behavior. The processor (P) 103 is furtheradapted to iii) monitor the number of learning related data where thepre-defined time interval limit has been reached and based thereonselect a task to be presented to the user by the output means.

The task may be formed by one or more learning related data including atleast one learning related data where the pre-defined time intervallimit has been reached. The processor may carry out a further step iv),namely to select the task in accordance to a set of rules includingselecting at least one of the remaining learning related data in thetask in accordance to the associated time intervals so as to optimizeand individualize the task to the user. The different tasks present in away multiple learning levels where each level presents one type ofexercise, e.g. pronunciation exercise of a single word, wherein withinthis pronunciation level ascending time intervals are implemented if theuser answers correctly or good enough. Lets say that the pronunciationfrom the user of a given word is rated as acceptable using e.g. arecognition system that rates the quality of the pronunciation from theuser, a t_(t) indicator is associated to this pronunciation if the usersperformance was acceptable. Accordingly, this first task is presented tothe user with multiple of exercises having ascending time intervals (incase the user answers correctly) spaced based on a first task forgettingcurve. In case the user's response to previous exercise has anassociated true task exercise indicator (t_(y)). If the answer isincorrect a false task indicator (t_(f)) is associated to the task. Thetask forgetting curve is calculated based on an empirical study for eachtask. The curve is a function of coefficients that may include but notnecessary limited to c_item_group(task), c_group(task), c_item(task) andc_person(task). These aforementioned coefficients are explained in thelist below:

-   -   c_item_group(task) represents the coefficient for that item        based on a group of users. For example this coefficient for that        item for the pronunciation task might be low if the selected        group of users have in general had hard time when pronouncing        that particular item and therefore in general got a low score        when pronouncing that item.    -   c_group(task) represents the coefficient for that task based on        a group of users. For example this coefficient might be low for        the pronunciation task if the selected group of users have in        general had hard time when pronouncing a group of items and        therefore got a low score when pronunciation those group of        items.    -   c_item(task) represents the coefficient for that item for that        person for that particular task. For example this coefficient        for an item for the pronunciation task might be low for the user        if the user has often had hard time repeat a pronunciation        exercise for that particular item and therefore has got a low        score in a pronunciation exercise for the item. The starting        value for a new user for this particular coefficient might be        the value of the c_item_group(task).    -   c_person(task) represents the coefficient for the person for        that particular task. For example this coefficient for        pronunciation might be high for the user if the user often gets        high score when exercising pronunciation.        A similar set of coefficients may as well be used to calculate        the forgetting curve for the flashcards. These coefficients may        be different for each language pair.

When the word has been further spaced into memory via the flash cardprocess a second task milestone is reached based on empiricalmethod/process of the second task. This procedure is the repeated formultiple of exercises for this second task, which again are individuallyspaced based on the second task forgetting curve.

This may then be repeated for third, forth etc. task where the multipleof exercises within each task are spaced based on the third, forth etc.forgetting curves. The ascending time intervals between the exerciseswithin the same task are preferable individualized for each individualuser. Further, the ascending time intervals within different task may bedifferent because the user may e.g. be stronger in the pronunciationtask than in the listening task. Accordingly, the system is capable of,based on the user's history, to customize the task to the user.

Referring to the flash card procedure, in one embodiment, the processor(P) carries out a further step v), namely to monitor the f_(l)indicators in the storage space and based thereon repetitivelypresenting learning related having associated f_(l) indicators to theuser with user-specific time intervals until t indicators are issued forthe user's 105 response. These t_(l) indicators are then subsequentlyassociated to the learning related data and the associated response datain the storage space 109. The processor then repeats step i)-iv) asdiscussed above.

Steps i)-v) will be discussed in a more detailed way in relation to theflowchart in FIG. 2.

In one embodiment, the language learning system 100 is comprised in aregular computer device 106 (e.g. PC or laptop computer) comprising saidoutput and input means 101, 102 and said processor 103. The database 104may either be located at the computer device side 106, or it may be anexternal database 110 that hosts such data for thousands of users eachbeing associated with storage space 111 with different user IDs. In thiscase the communication between the computer 106 and the database 110occurs via a communication channel 108 which may be publicly orprivately accessed network (wired or wireless) such as the Internet or amobile network such as 3G. The processor (P) 103 may also be a remoteprocessor comprised in an external computer or a server 112 thatoperates the external database 110.

In another embodiment, the language learning system 100 is comprised ina portable device 107 such as mobile phone, a tablet computer, and thelike that is capable of communicating via said communication channel 108to the storage space 111 in the external database 110. The processor mayeither be provided at the mobile device side, or at said externalcomputer/server 112.

FIG. 2 shows a flowchart of a method according to the present inventionfor personalizing language learning to individual users.

In step (S1) 201, learning related data are generated and presented to auser associated with a user ID. This may as an example includedisplaying visually a flashcard to the user, e.g. be a Japanese entry“tomodachi” that is displayed on e.g. the user's computer screen or themobile phone screen. The learning related data that are presented by theuser are in one embodiment data that the user has pre-selected in e.g. apre-selection mode where the user selects several words of interest. Ifas an example the user is an English native speaking pilot that ismoving to Japan and he/she want to learn Japanese, the user can start byentering several words that have something to do with being a pilot,e.g. an aircraft, captain, stewardess etc. plus some general words suchas “friend”. By doing so, the system automatically generates severalflashcards with these pre-selected words where on “one side” theJapanese words is given and “on the other side” the English translation.By doing so the user himself can decide how to place the emphasis on,i.e. in this case on words relating to flight. Accordingly, the usergenerates his/her own direction in the language learning.

In another embodiment, the user can indicate that he/she want to startwith general beginner course. In such case, the system willautomatically select which flashcards are most suitable for the user.

In step (S2) 202, response data are received from the user in responseto this learning related data indicating the user's response. In anembodiment and referring to the example in S1, this works in a way thatthe user takes some time to think about what the answer is to“tomodachi” and then requests the answer to be shown by e.g. clicking on“show correct answer” icon on a display. If the answer that is shownmatches the answer that he/she thought that was the right answer theresponse data received from the user is simply input data saying whetherthis answer being shown matches the user's answer. This may e.g. be doneby clicking on a “right” or “wrong” button displayed with the flashcard.

In step (S3) 203, the user's response data, i.e. provided by clickingsaid “right” or “wrong” button, displayed with the flashcard.

In step (S4) 204, the learning related data and the associated responsedata is stored in a database including a storage space associated to theuser ID so as to generate an individualized language knowledge databasefor the user. As discussed in relation to FIG. 1, this database can bean external database that is operated by an external server, or be alocal database comprised in e.g. a PC computer or a portable device.

In step (S5) 205, a true (t_(l)) or false (f_(l)) indicators are issuedindicating whether the response data matches the learning related datapresented to the user. These indicators are subsequently associated tothe learning related data and stored at the storage space. Referring tothe example above if the user thought that the answer to the Japaneseentry “tomodachi” is “friend”, and the correct entry that is displayedis “friend” the user selects said “right” flashcard and a t_(l)indicator is issued and associate to this entry “tomodachi”. Steps S1-S5are repeated for a number of entries where such t_(l) or f_(l)indicators are issued and associated to learning related data and theresponse data and in that way a user specific database is built up thatindicates the user's knowledge. Therefore it is possible to keep trackof the user data history in said storage space, which becomes moredetailed and gives a better insight into the user's improvements.

In step (S6) 206, the t_(l) indicators are monitored and based thereonlearning related data having associated t_(l) indicator are repetitivelypresented to the user with said user-specific memory spaced intervalwhich is registered and associated to the learning related data. Eachtime it is checked whether this memory spaced interval has reached apre-defined time interval limit, or a first task milestone. Thispre-defined time interval limit may vary from user to user and also bedifferent depending on the difficulty level of the entry as an example.If this pre-defined time interval limit has not (N) yet been reached,step (S6) 206 is repeated but with increased time interval until thispre-defined time interval has been reached (Y).

In step (S7) 207, the number of learning related data are monitoredwhere this pre-defined time interval limit has been reached and basedthereon a task (pronunciation, listening, reading, writing,conversation, etc.) is selected for the user. Accordingly, at this levelit is assumed that the user is ready to exercise the entry at a moreadvanced level.

It should be noted that at (S7) a first task is start parallel to theprocess described in (S1)-(S6), but this process proceeds continuouslyparallel to the task process (S7) and subsequent task process. Thismeans that e.g. four task processes plus the flash card process arerunning simultaneously is parallel fashion.

In a preferred embodiment, the exercises within the task are formed by aone or more learning related data including at least one learningrelated data where the pre-defined time interval limit has been reached.

In one embodiment, the task is selected in accordance to a set of rulesincluding selecting at least one of the remaining learning related datain the task in accordance to the associated user-specific time intervalsso as to optimize and individualize the task to the user.

In one embodiment, the learning related data are selected from one ormore entries and a sample sentence selected from sample sentences storedat the storage space is presented to the user.

In one embodiment, the task in (S7) may further include receiving aspeech input from the user by reading a sentence including the word infocus, where the speech input is subsequently processed and comparedwith a pre-stored reference speech and based thereon the pronouncingfrom the user is rated.

In one embodiment, the learning related data are entries and a soundfile is selected and played from sample sound files stored at saidstorage space via said output means.

In one embodiment, the learning related data are entries and the task isto select and play a sound file from sample sound files stored at thestorage space and further to present the content of the sound filevisually to the user as a sentence(s) such that at least one learningrelated data where the pre-defined time interval limit has been reachedis presented in the sentence as a blank. In this embodiment, the taskincludes receiving input from the user so as to fill up in the blank andsubsequently process and compare the received input with a referencelearning relating data.

In one embodiment, the learning related data are grammar points andwhere the intent is to present grammar points in an example sentence(s)or speech. This may also be performed while presenting flashcardentry(ies). It is thus possible to knit grammar into this languagelearning.

In one embodiment, the learning related data are entries and the intentis to trigger a conversation between the user and an instructor. Theconversation is preferably selected such that it includes at least oneentry where the pre-defined time interval limit has been reached, i.e. atask milestone.

In one embodiment, the learning related data are one or moreentrie(s)/sentence(s)/figure that are presented visually to the user andwhere the intent is to receive either an entry or a sentence(s), or aparagraph(s) as a response from the user including at least one entrywhere the pre-defined time interval limit has been reached, i.e. a taskmilestone. This may be done via keyboard commands or via speech.

Example for Sentence Selection:

Sentence B that includes w1 w2 w3 is chosen instead of sentence A, whichincludes w1 w4 w5, where w is an entry such as a single word. Thissentence may be presented to the user visually (displayed on e.g. thecomputer screen) or via speech, or presented to the user for readingwhen exercising pronunciation. Both these sentences contain the entryw1, which is the entry in focus, i.e. the entry where the memory spacedinterval has been reached. However, the system favors B over A becausew2 and w3 have user-specific time-intervals that are more favorable thenthe memory spaced interval for w4 and w5. In that way, the informationabout the time intervals, which have not yet reached the pre-definedtime interval, are being used as input data in selecting the mostfavorable sentence.

This sentence B may be selected out of potentially thousands or hundredsof thousands sample sentences stored at said database. This sentence Bmay also be considered as being the best example sentence to be used ine.g. i) paragraph etc. used to aid the user when reviewing a certainflashcard, ii) paragraph etc. presented when in pronunciation mode(speech recognition might be used here to correct the user but it is notnecessary), iii) paragraph etc. used in listening mode, or iv) paragraphetc. used in reading mode. “flash card” process or in the “task”processes, e.g. pronunciation.

In one preferred embodiment, the method further comprises the step ofmonitoring the f_(l) indicators in said storage space and based thereonrepetitively presenting learning related data having associated f_(l)indicators to the user with user-specific time intervals until t_(l)indicator are issued for the user's response and subsequently associatedto the learning related data and the associated response data in thestorage space. This would typically be to start with descending timeinterval where the interval is systematically adjusted to the user byrepetitively decrease this time interval for e.g. a specific entry whichmay be difficult for the user to remember until the user remembers it.Said steps (S1)-(S6) are then repeated.

FIG. 3-8 show a user being presented with a learning related data, i.e.the user is in the flash card process.

FIG. 3 depicts graphically the scenario where an English native speakinguser is learning Icelandic. Generally, when the user is starting a newlanguage course, e.g. in Icelandic, he/she can select via appropriateselection function (not shown here) in what area/topic the user isinterested in. Based thereon, the language learning system selects thelearning program. As discussed in relation to FIG. 2, the user may alsomanually enter several entries of interest and based thereon the systemautomatically selects the language direction for this individual user.The language learning system may also via artificial intelligence findout in what area the user is interested in and based thereon selects themost suitable language learning program for the user.

At the very beginning (not shown here) the user is presented with thelearning related data, e.g. a word, and the answer and is asked tomemorize. Referring to this example after this first step an Icelandicentry 301 is displayed via flashcard to the user. After some time ofthinking the user comes up with the conclusion that this word stands forthe word “house” in English. The user now pushes (e.g. via mouse click)the “Answer” 302 button to see the correct answer, which as depicted inFIG. 4, shows that the answer is indeed the word “house”. The user cannow indicate whether his/her answer was correct or not by selectingeither one of right (R) 304 or wrong (W) 305 buttons. By selecting the“R” 304 button (e.g. via mouse click) the user indicates that his/heranswer was correct. By doing so a t_(l) indicator is issued, in otherwords a Boolean true state is set, e.g. by issuing a trap to theprocessing unit indicating that the user had the correct answer and thist_(l) indicator is associated to the word “hits” and stored in thestorage space associated to this user. The same applies when the userselects the (W) 305 button, where instead of right indicator a false(f_(l)) indicator is issued. Other scenarios are of course possible,such as by displaying via the flashcard several answer possibilities andlet the user select out the answer for this flashcard.

This is continued for some number of entries to start with. After that,the time interval between repeating the question for this given entry isincreased, from e.g. 5 minutes to 10 minutes, where after each timeinterval the user answers the question. This may be done for severalparallel running entries. When this time interval, where the user stillis answering the question correctly, has reached said pre-define timelimit (a first task milestone), e.g. 20 minutes, the language learningsystem decides to move the learning for this specific entry to a moreadvanced level, namely to select a first task. The first task may beexercise the pronunciation of the word “house” by letting the user readthe word aloud, in this case the word “hús”. A speech recognition systemmay be provided for processing the speech from the user. The memoryspaced interval may be from few minutes up to days or weeks, months oreven more. The methodology within the different parallel running taskshas already been discussed in relation to FIGS. 1 and 2.

FIG. 4 depicts graphically the scenario from FIG. 3, where additionally,a sentence 303 has been selected from a large number of pre-storedsample sentences, which includes the entry “house” in an English samplesentence. In that way the user gets an opportunity to exercise thisentry via this sentence in a more advance manner. As discussedpreviously, the selection of this particular sentence is carefullydecided with the aim of selecting a sentence that is most suitable forthe user at this specific time point. If w1=“

etta”, w2=“er” and w3=“hús”, where w3 is the entry in focus where saidmemory spaced interval has been reached, the remaining entries w1 and w2are entries selected so as to optimize and individualize this processfor to the user. These entries may e.g. be selected based on that theuser has some pre-knowledge about these entries in relation to said usermemory spaced time interval, or if the sentence is very complicated,based on that the user possesses good knowledge about these entries inrelation to said user memory spaced time interval but has still notreached said pre-defined time limit. As an example, w1 may be an entrywhere the user memory spaced time interval is e.g. 8 days and w2 wherethe user memory spaced time interval is 2 days. Still, neither w1 nor w2are entries where the pre-defined memory time interval limit has beenreached, which could e.g. be 12 days for w1 and 3 days for w2.Accordingly, the user-specific memory intervals that are associated atall times to these entries are used as additional input data inselecting the optimal sample sentence for the user. This sample sentence303 could also be played out aloud for the user via a speaker 402 at thesame time. As discussed previously in relation to FIGS. 1 and 2, aspeech recognition system could be provided to process the speech fromthe user so as to determine how good the pronunciation for the entry infocus w3 actually was.

FIG. 5 depicts graphically the scenario from FIGS. 3 and 4 whereadditionally the grammar is being exercised, in this case thedeclination of the entry house in Icelandic is shown. Also, this couldbe played out aloud for the user.

In an embodiment, several grammar possibilities may also be displayed(not shown here) for the user via e.g. answers a) to d), each of whichshowing different solutions where only one is the correct one, where theuser is supposed to select the answer that shows the correct declinationof the entry “house” in Icelandic.

FIGS. 6-8 depict the scenario where the language learning is vice versecompared to FIGS. 3-5 for the same English native speaking user, i.e.the entry “house” is being exercised in the other direction, i.e. froman English entry to Icelandic entry.

FIG. 6 depicts additionally the scenario where the entry in focus ismissing 601 in an Icelandic sentence. Again, the user now thinks aboutwhat the correct entry is and clicks the “Answer” 302 button to see theanswer as shown in FIG. 7, and subsequently the right (R) 304 or wrong(W) 305 buttons to indicate whether the answer was correct or not. Thisscenario is actually identical to the scenario discussed in relation toFIGS. 3 and 4, but other way around.

The language learning preferably goes both ways, e.g. from English toIcelandic and Icelandic to English, although said user specific timeintervals and said time interval limits may differ from each other forthe same word but in different language.

FIG. 7 shows the scenario where a sample sentence 701 has been selectedfrom a large number of pre-stored sample sentences, which includes theentry “hits” in an Icelandic sample sentence, and FIG. 8 depicts thescenario where the grammar is exercised via said sample sentence 801, inthis case the declination of the word house. These sample sentences 701,801 could also be played out loud for the user via a speaker 402.

FIGS. 9 and 10 depict a scenario where a conversation task, which may beconsidered as being the highest task level, has been initiated between auser and an instructor, where FIG. 9 is seen from the user's view andFIG. 10 is seen from the instructor's view. In this case, the user thatmay e.g. be learning Japanese possesses sufficient knowledge toparticipate in such a conversation, which is organized so that theconversation includes at least one entry where the said pre-defined timeinterval limit (task milestone) has been reached. This entry may bevisualized to the instructor who therefore knows which entries are“ready” for the student to use, i.e. have reached said pre-defined timelimit, and based thereon tries to direct the conversation towards atopic that includes this word. By doing so a spontaneous usage of theentry will be exercised with the aim of training the user to use thisentry spontaneously without even noticing it. The instructor marks theentry as “accomplished” only when the user has used the entry correctlyin the conversation. The user can see entries that have been markedaccomplished in the user interface.

FIG. 11 depicts one embodiment of the language learning system accordingto the present invention, where a part of the system is implemented as acomputer game or as an “app” that may be downloaded from e.g. theinternet. In this game randomly organized letters are presented to theuser as shown here in a manner that is personalized for this user, wherethese letters are pre-selected in a way that they can form an entry thatthe user has just learned or entries that have pre-time interval timelimit that has been reached (i.e. a task milestone) that the systemdeems are useful for the user to exercise in the game. One way of doingso is to construct a puzzle from entries that are still not forgotten(time limit has not been over passed for the word).

If the user has two entries that the system deems are useful to be usedin the game, e.g. the entry “car” and “law”, then the letters beingdisplayed on e.g. the computer/mobile phone screen are selected suchthat they include letters that can form the entry “car” and/or “law” andeven other types of entries that are not in focus. If the user findssuch entries he/she get points. As shown here below, this could bevisualized for the user in the following way:

c a w

s a r

l v r

The aim of the game is thus to check if the user can find out the word“car” by pressing with his finger on “c”, then “a”, then “r”. By doingso he/she gets certain number of points. The same applies for the entry“l”, “a” and “w”, i.e. law. The user would then get more points. Theentry may be highlighted for a second and then the user gets his bonuspoints since he/she found an entry that are in focus of the game. If theuser is able to detect a word the game may notify the system, that mayaffect either the user history for memory retention and its coefficientsand or update one or more of the task intervals and task coefficientsfor that particular word.

FIGS. 12 and 13 depict an example where a user is learning Japaneseusing an easy flashcard level.

In this scenario, the language learning system does not start withdisplaying complicated signs, but instead displays these entries withhis/her letter system. In FIG. 12 the Japanese entry “ato” 1201 is shownin a first step. After some time of thinking the user clicks on the“Answer” button 302 resulting in that the meaning of this entry is shownin English, i.e. “scar” or “trace” 1301 in FIG. 13. As discussedpreviously in relation to FIGS. 3-8 the user now clicks on the right (R)304 or wrong (W) 305 buttons depending on whether his/her answer wascorrect or not triggering said t_(l) or f_(l) indicators. FIG. 13depicts also the scenario where the word “ato” is being used in asentence 1302. As discussed previously, the remaining entries in thissentence would be carefully selected so as to find the most optimalsentence from e.g. thousands of sample sentences for the user.

FIG. 14 shows an embodiment where a more advanced level of the flashcarddescribed in FIG. 12 and FIG. 13. As shown here, the Japanese

, hiragana version of the word “ato”, 1401 is exercised. After some timeof thinking, the user clicks on the “Answer button” 302 to see the rightanswer 1502, which in this case is the word “scar” or “trace” 1502. FIG.15 shows also where this entry is being used in sample sentence 1503selected in a way as discussed previously, where this sample sentencemay additionally be played out aloud for the user via said speaker 402.

FIG. 16 depicts the scenario for a more advanced level of the flashcardthen described in FIG. 14 and FIG. 15. In this scenario the entry isdisplayed with Japanese kanji symbol. The entry may include more thanone kanji symbol or a mixture of kanji symbols and hiragana symbols. Inthis case, the kanji symbol

is being used instead of

. The transfer from said Japanese letters in FIGS. 14-15 to thisJapanese sign “mode” may e.g. be initiated via simple selection mode bye.g. pressing an appropriate button inicating that the user wants tomove to a more advanced level. By doing so the flashcard is “upgraded”and put to a more advanced level with more complicated Japanese signs.Again, after some time of thinking the user clicks on the “Answer”button 302 resulting in that the meaning of this entry is shown.

FIG. 17 shows the answer both via the simple Japanese hiragana symbols1705 and more complicated kanji symbols 1701 as well as the Englishmeaning of this entry 1702. Again, the user can via said right (R) 304and wrong (W) 305 buttons indicated whether the answer was correct ornot. Accordingly, in this figure a kind of a two step approach isinitiated, namely to show the sign

both as Japanese letters as well as the meaning in English. Shown isalso a sample sentence 1703 selected as discussed previously.

It should be noted that the user can at any time “downgrade” theflashcards and go back to the easier level with only the Japaneseletters as shown in FIGS. 14 and 15. The sample sentence or the entriesmay additionally be read out aloud via said speaker 402.

FIG. 18 depicts an example of so-called forgetting curve for newlylearned information, where the horizontal axis is time t and thevertical axis is the retention R in percentage. This figure may also beunderstood as showing how entries travel deeper into memory as afunction of time.

The first learned entry 1801 is shown and the curve over the timeinterval 0-1 depicts how fast the user forgets this entry. Forsimplicity, assume that one unit in time is one day (could just as wellbe e.g. 5 minutes). Then, after one day it is according to this figure80% likelyhood that the user remember this word. The next day, this sameentry is reviewed (first review) the time until this retention fallsdown to 80% is two units in time, i.e. from 1-3, and a second review thetime until the retention falls down to 80% is three units in time, orfrom 3-6. As an example to explain time interval (deep in memory) thenit is 2 units deep at t=1.

This figure depicts graphically the principle of said user-specificascending memory time intervals, where the memory time interval iscontinuously increased so as to store the entries deeper in the brain.After some given time, the time interval (i.e. units of time) gets solong and the slope of the curve approaches a flat line that one can saythat word is stored for years or decades (until the retention falls downto 80%).

In a preferred embodiment, the present invention aims at finding theoptimal percentage, which can be, but is not limited to, 80% percentagemark for each flashcard for each individual user in order to minimizethe time that the user spends on reviewing flashcards. The aim is alsoto try to find the optimal time-limit for each task, i.e. pronunciation,listening, reading, writing, conversation, etc. for each entry in orderto optimize the learning speed and therefore minimize the time needed togain proficiency.

FIG. 19 depicts the general principle of the present invention, wherethe upper horizontal axis stands for user-specific ascending memoryspaced intervals 1901-1905 which is a non-linear timeline (or spacedinterval time) and the vertical axis on the left side depicts the userinterface 1906-1912. It should be noted that this figure applies for oneuser, but this figure is different between different users.

The user 1913 may at the very beginning select “Add words” function 1906where he/she can start with entering several words within his/herinterest. Referring to the previous example, assuming the user is apilot and is moving to Japan for some time, he/she would like toincrease his/her knowledge in words that have something to do with beinga pilot. By entering words like “aircraft”, “captain”, “sky”, “speed”etc., the system automatically generates flashcards for these words. Thesystem may also suggest words to the user based on the words that theuser and possibly other users have searched and added to theirdatabases.

On the first day in the flash card process, the first memory spacedinterval 1901 spans five minutes, i.e. 5 minutes pass from where theuser is shown a given entry in Japanese for the first time until theentry is repeated. This interval is now increased up to 30 minutes 1902,i.e. it is now checked whether 30 minutes can pass and the user stillremembers this entry. It should be noted that several entries could beshown parallel to this given entry. Each time the user marks right orwrong indicating whether he/she had the word right or not, and each timesaid t_(l) or f_(l) indicators are issued and associated to the entries.As shown in this example, if the user remembers the entry, the thirdmemory spaced interval 1903 will be 24 hours and the fourth memoryspaced interval 1904 2 days. Within these four time intervals 1901-1904the pronunciation task is being exercised parallel to the flashcardprocess for each item. For this given user, the system may decide thatsaid pre-defined time limit is two days for a listening task 1909milestone, which may activate a listening task.

Since this entry has now reached the pre-defined time limit of two days,but this time may vary between different users or different entries, anadditional task is presented to the user 1913, which in this example islistening 1909, and is run parallel to the other task. As discussedpreviously, a sentence may be selected from thousands of samplesentences where this entry, i.e. “aircraft” is included. The selectionof such a sample sentence has already been discussed in relation toFIGS. 2-5.

As discussed previously, within each task there are task-specificexercises each of which have their own time task ascending intervalsbased on the task forgetting curves.

In one embodiment, such as listening may also be presented with saiduser-specific ascending exercised time interval or, if the user hassuddenly forgotten the entry in focus, to descend the exercise timeinterval. In that way, time exercise intervals, either increasing ordecreasing, may be utilized during the listening task. Let's say thatthe user has in this listening task forgotten the word in focus, thesystem may automatically select a new sentence, which is simpler butincludes the word in focus and present it to the user. Thus, the systemdoes not necessarily use the same sentence(s) during this process.

When the word in focus is 5 days deep in memory a new task is presentedto the user, e.g. when said pre-defined time interval limit (taskmilestone) has been reached, which is reading task 1910. In this readingtask where e.g. news, articles, etc may being selected which include theword in focus (i.e. reached the pre-defined memory spaced intervallimit). Such news may e.g. story about the new Airbus aircraft, how manypassengers it can transport etc.

When the word in focus is ten days deep in the users memory, the user1913 is presented with a writing task where the entries in focus areshown to the user. This writing task runs parallel to the previoustasks. The user is then supposed to write a text that includes theentries in focus. Also, at this time point, a grammar may as an examplebe exercised. The user then “sends” the text for approval, which may bedone with the assistance of a human, or computer instructor, which deemsweather the entries have been used correctly and with other comments ifany on the text. After the text has been reviewed the text is sent backto the user who is now able to see the mistakes and make correctionsaccordingly and send back. This process can be iterated until the textis correct.

The final task in the example is the conversation task from as discussedpreviously in relation to FIGS. 9 and 10 where a conversation isinitiated between the user and an instructor, where the instructor ispresented with the entries that are in focus. In this case the entry“aircraft” is used to select the focus of the conversation such thatthis entry will be used spontaneously by the user.

An alternative way to present the present invention is the followingexample:

Begin example:

This example illustrated our solution for a language learning systemaccording to the present invention:

-   -   D1. Personal word spacing: Each person has his/hers own personal        forgetting-curve coefficient that is a measure of how easily a        person remembers new things. This system determines each        language pair forgetting curve, it further determines each        person's forgetting-curve coefficient based on their results in        memorizing new words. Words are categorized into difficulty        levels, based on how difficult they are to remember, and each        level has its own difficulty coefficient. Personal spacing is        found by combining the current memory retention of a particular        word, the personal forgetting curve coefficient for that        particular word, the general personal forgetting-curve        coefficient and the general word difficulty coefficient that is        harvested from responses to that word in the whole user        database, to project the next review time.    -   D2. Personal task spacing: Five different tasks, listed but not        limited to a.-e., are currently used in the system, each with a        special task-coefficient linked to a person:        -   a. Pronounce        -   b. Listen        -   c. Read        -   d. Write        -   e. Conversation    -   The task-coefficients are found by an empirical method that        determines when the average person is ready to perform a task        listed above. Below are the task-coefficients values:    -   β_(P)=t1    -   β_(L)=t2    -   β_(R)=t3    -   β_(W)=t4    -   β_(C)=t5    -   To determine when a person is ready to perform a task, i.e. the        task milestone, for a selected word, the system combines and        fits the word's current memory depth, word difficulty        coefficient, the personal forgetting-curve coefficient and the        corresponding task-coefficient to a probability distribution. So        for example when a person with an average memorizing ability is        working with an average difficult word, the listening task would        be scheduled when the word is β_(L) minutes deep in memory.    -   D3. Task-forgetting curve: Each of the tasks has its own        task-forgetting (repeatability) curve that is different from the        memory forgetting curve. So in this example after having        performed the listening task once, it is scheduled again by        fitting the personal task forgetting-curve coefficient to the        corresponding task-forgetting curve combined with the word task        difficulty coefficients.

Let's follow one word through the whole process:

-   -   1. The word for “friend” in Japanese, “tomodachi”, is added.    -   2. When entering flashcard learning mode the user is asked to        memorize the word “tomodachi” for “friend”.    -   3. After few moments the system prompts the user with a new        flashcard and asks for the meaning of “tomodachi”. The user can        request an example sentence were the missing word is “tomodachi”        to aid him to remember the word. When having determined the        meaning of the word the user presses show answer. The user now        selects true or false depending on whether he/she knew the        answer or not. Depending on the user's response the system        calculates the word's memory retention with the algorithm        described in D1.    -   4. The system now has one review process going for the word        “tomodachi”, the memory retention process and the user is        prompted with the flashcard for “tomodachi” in intervals based        on his memory retention for the word “tomodachi” calculated each        time according to D1.    -   5. The system also has a task scheduler running that monitors        when the task review processes are to be started i.e. a task        milestone has been reached. So when for example the threshold        for the pronunciation task is reached, calculated as described        in D2, the pronunciation task process is started and the users        gets a pronunciation task for the word “tomodachi”. After having        completed the task the first time the system now calculates the        pronunciation tasks interval according to D3.    -   6. The system now has two review processes going for the word        “tomodachi”, the memory retention process and the        pronunciation-task retention process, each running        independently.    -   7. This continues until all five task processes explained in D2        are running and the system is running six independent processes        (the memory retention process plus the five task processes        explained in D2) for the word “tomodachi”. These processes        continue indefinitely.

End example.

After some usage of the system (could be several days or weeks) the usermay come back to the system seeing that there are different tasks thatare waiting to be visited. The list of due items for the flashcardreviews as well as the tasks may be as follows: Flashcard reviews 234cards, pronunciation tasks 23 items, listening tasks 34 items, readingtasks 12 items, writing tasks 6 items, conversation tasks 12 items. Theuser can choose the order of the tasks. He/she may for example startwith conversation, stop after completing 5 items and then go intoreading. The system can also be put into “autopilot” mode where thesystem recommends the order of the tasks and its items for the user foroptimal results. This may be based on how much time the user tells thesystem that he/she can devote on the system for the day, for the week orother periods.

While the invention has been illustrated and described in detail in thedrawings and foregoing description, such illustration and descriptionare to be considered illustrative or exemplary and not restrictive; theinvention is not limited to the disclosed embodiments. Other variationsto the disclosed embodiments can be understood and effected by thoseskilled in the art in practicing the claimed invention, from a study ofthe drawings, the disclosure, and the appended claims. In the claims,the word “comprising” does not exclude other elements or steps, and theindefinite article “a” or “an” does not exclude a plurality. A singleprocessor or other unit may fulfill the functions of several itemsrecited in the claims. The mere fact that certain measures are recitedin mutually different dependent claims does not indicate that acombination of these measured cannot be used to advantage. Any referencesigns in the claims should not be construed as limiting the scope.

1-17. (canceled)
 18. A language learning system adapted to personalizelanguage learning to individual users, comprising: an output means forgenerating and presenting learning related data to a user associatedwith a user ID; an input means adapted to receive, in response to saidlearning related data, response data from the user indicating the usersresponse to said learning related data; a processor for associating saidlearning related data to said response data so as to couple the responsefrom the user to said learning related data; and a database including astorage space associated to the user ID for storing said learningrelated and said associated response data and thus generating anindividualized language knowledge database for the user; wherein theprocessor is further being adapted to: issue a true (t_(l)) or false(f_(l)) learning related data indicator indicating whether the responsedata matches the learning related data presented to the user, the t_(l)or f_(l) indicator subsequently being associated to said learningrelated data and stored at said storage space; monitor said t_(l)indicators in said storage space and based thereon repetitivelypresenting learning related data having associated t_(l) indicator tothe user with user-specific ascending memory spaced interval registeredand associated to the learning related data until a pre-defined timeinterval limit has been reached, and based thereon select at least onetask to be presented to the user by said output means; wherein withinsaid at least one task plurality of task specific exercises each ofwhich includes at least one learning related data where said pre-definedinterval has been reached are presented to the user with task timeascending intervals in case a user's reply to previous task specificexercises is correct or satisfactory.
 19. The language learning systemaccording to claim 18, wherein said process of presenting learningrelated data by said output means includes presenting the learningrelated data in a first language and in a second languagesimultaneously.
 20. The language learning system according to claim 19,wherein subsequent to present said user with said learning related datain said first and second languages the user is presented with saidlearning related data in a first language and at least one suggestionentry of a learning related data in said second language, said inputfrom the user via said input means being whether said suggestion entrycorresponds to said learning related in the second language.
 21. Thelanguage learning system according to claim 18, wherein said at leastone task is run parallel and independent from said process of presentinglearning related data by said output means, said at least one task beingrun independent from each other such that while presenting said userwith said task specific exercises learning related data are presented tothe user simultaneously.
 22. The language learning system according toclaim 21, wherein said processor is operable to start a new taskparallel to said process of presenting learning related data andparallel to the task already being run, said decision of starting saidnew task being based on monitoring said user-specific ascending memorytime interval and utilize said memory spaced as an indicator of how“deep enough” the learning related data is in the user's memory.
 23. Thelanguage learning system according to claim 18, wherein said task timeascending intervals are spaced individually based on task specificforgetting curves each of the task specific forgetting curves beingcharacteristic for each individual task.
 24. The language learningsystem according to claim 18, wherein for each individual user, saidprocessor is further operable to utilize said learning related datapresented to the user to determine a forgetting-curve coefficients foreach task, the forgetting-curve coefficients being indicative of howeasily each individual user remembers new learning related data.
 25. Thelanguage learning system according to claim 18, wherein said exerciseswithin said tasks are formed by a multiple of learning related dataincluding at least one learning related data where said pre-defined timeinterval limit has been reached, the processor further being adapted to:select an exercise in accordance to a set of rules including selectingat least one of the remaining learning related data in said task inaccordance to said associated user-specific time intervals so as tooptimize and individualize the exercises to the user, receive, inresponse to exercise presented to the user, response data from the uservia said input means indicating the users response to said exercise;issue a true-task (t_(t)) or false-task (f_(t)) indicator indicatingwhether the response data to said exercise is correct or not, the t_(t)or f_(t) indicators subsequently being associated to said task orexercise and stored at said storage space; and selecting at least onesubsequent exercise based on said t_(t) or f_(t) indicators.
 26. Thelanguage learning system according to claim 18, wherein the exerciseswithin said at least one task are adapted to the individual users withvariable time intervals bases on said t_(t) or f_(t) indicators.
 27. Thelanguage learning system according to claim 18, wherein said at leastone task is selected from at least one of the following: a pronunciationtask and where said task specific exercises include playing at least onelearning related data where said pre-determined interval has beenreached to the user and where the user repeats the pronunciation of saidlearning related data, said language learning system further comprisinga speech recognition system for processing the pronunciation from theuser so as to determine if the pronunciation is correct or not, where incase the pronunciation is correct a t_(t) indicator is associated theword, or listening where the exercises include displaying a sentence tothe user comprising at least one word where said pre-defined timeinterval limit has been reached in the sentence(s) is blank, where thesaid input means is a key or touch button command where the user repliesto the missing word in the blank, the input subsequently being processedand compared with a reference relating data where in case the replymatches with the missing word a t_(t) indicator is associated to secondtask; reading task where the exercises include presenting the user witha paragraph including at least one learning related data where saidpre-defined time limit has been reached; a writing task and where saidexercises include that the user writes a sentence including at least oneword where said pre-defined time interval has been reached, saidsentence subsequently being processed and compared with a pre-storedreference sentences, and where t_(t) or f_(t) indicators are associatedto said task depending on a match or non-match with said referencesentences; a conversation task and where the exercises in theconversation task include initiating a conversation between the user andan instructor via a network, the instructor being provided with userspecific information including information about pre-defined timeinterval limit has been reached, the subject of the conversation beingselected such that it includes at least one learning related data wheresaid pre-defined time interval limit has been reached; a combination ofone or more of the above mentioned tasks.
 28. The language learningsystem according to claim 18, wherein the processor is further adaptedto monitor said f_(l) indicators in said storage space and based thereonrepetitively presenting learning related data having associated f_(l)indicators to the user with user-specific time intervals until t_(l)indicators are issued for the users response, the t_(l) indicatorssubsequently being associated to said learning related data and saidassociated response data in said storage space, said step of presentinglearning related data having associated t_(l) indicator to the user withuser-specific ascending memory time interval being repeated until apre-defined time interval limit has been reached.
 29. A method ofpersonalizing language learning to individual users, comprising:]generating and presenting learning related data to a user associatedwith a user ID; receiving, in response to said learning related data,response data from the user indicating the users response to saidlearning related data; associating said learning related data to saidresponse data so as to couple the response from the user to saidlearning related data; and storing said learning related and saidassociated response data in a database including a storage spaceassociated to the user ID so as to generate an individualized languageknowledge database for the user; wherein the method further comprises:issuing a true (t_(l)) or false (f_(l)) learning related data indicatorindicating whether the response data matches the learning related datapresented to the user, the t_(l) or f_(l) indicators subsequently beingassociated to said learning related data and stored at said storagespace; monitoring said t_(l) indicators in said storage space and basedthereon repetitively presenting learning related data having associatedt_(l) indicator to the user with user-specific ascending memory spacedinterval which is registered and associated to the learning related datauntil a pre-defined time interval limit has been reached, and basedthereon select at least one task to be presented to the user by saidoutput means; wherein within said at least one task plurality of taskspecific exercises each of which includes at least one learning relateddata where said pre-defined interval has been reached are presented tothe user with task time ascending intervals in case user's reply toprevious task specific exercises is correct or satisfactory.
 30. Acomputer program product tangibly embodied on a computer-readable mediumand including executable code that, when executed, is configured tocause one or more processors of a computing device to: generate andpresent learning related data to a user associated with a user ID,receive, in response to said learning related data, response data fromthe user indicating the users response to said learning related data;associate said learning related data to said response data so as tocouple the response from the user to said learning related data; storesaid learning related and said associated response data in a databaseincluding a storage space associated to the user ID so as to generate anindividualized language knowledge database for the user; issue a true(t_(l)) or false (f_(l)) learning related data indicators indicatingwhether the response data matches the learning related data presented tothe user, the t_(l) or f_(l) indicators subsequently being associated tosaid learning related data and stored at said storage space; monitorsaid t_(l) indicators in said storage space and based thereonrepetitively presenting learning related data having associated t_(l)indicator to the user with user-specific ascending memory time intervalwhich is registered and associated to the learning related data until apre-defined time interval limit has been reached, and based thereonselect at least one task to be presented to the user by said outputmeans, wherein within said at least one task plurality of task specificexercises each of which includes at least one learning related datawhere said pre-defined interval has been reached are presented to theuser with task time ascending intervals in case user's reply to previoustask specific exercises is correct or satisfactory.